Emergency Vehicle Recognition Via Automated Smart Traffic Manager

The problem of everyday traffic congestion has been made worse by the growing number of automobiles in urban areas, which has caused substantial delays for commuters, students, and critical service providers like emergency vehicles. This study offers a novel Traffic Management and Emergency Vehicle Detection system in answer to this urgent issue. The modern YOLOv8 algorithm is used by this system to effectively detect objects on the road, and correlation filters are used to count the number of cars in each lane. This system's dynamic traffic signal control mechanism, which assigns time to each lane depending on the current vehicle density and successfully relieves traffic congestion, is its key component. This study's key conclusion is that the system's adaptive traffic signal control considerably reduces traffic congestion by enhancing vehicle flow. Additionally, it efficiently assigns priority lanes to emergency vehicles in traffic after correctly detecting them there.